Showing papers by "Ioannis Pitas published in 2001"

Robust audio watermarking in the time domain

Abstract: The audio watermarking method proposed in this paper offers copyright protection to an audio signal by time domain processing. The strength of audio signal modifications is limited by the necessity to produce an output signal that is perceptually similar to the original one. The watermarking method presented here does not require the use of the original signal for watermark detection. The watermark signal is generated using a key, i.e., a single number known only to the copyright owner. Watermark embedding depends on the audio signal amplitude and frequency in a way that minimizes the audibility of the watermark signal. The embedded watermark is robust to common audio signal manipulations like MPEG audio coding, cropping, time shifting, filtering, resampling, and requantization.

Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication

Abstract: A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.

Watermarking in the space/spatial-frequency domain using two-dimensional Radon-Wigner distribution

Abstract: A two-dimensional (2-D) signal with a variable spatial frequency is proposed as a watermark in the spatial domain. This watermark is characterized by a linear frequency change. It can be efficiently detected by using 2-D space/spatial-frequency distributions. The projections of the 2-D Wigner distribution-the 2-D Radon-Wigner distribution, are used in order to emphasize the watermark detection process. The watermark robustness with respect to some very important image processing attacks, such as for example, the translation, rotation, cropping, JPEG compression, and filtering, is demonstrated and tested by using Stirmark 3.1.

Image authentication techniques for surveillance applications

Abstract: In automatic video surveillance (VS) systems, the issue of authenticating the video content is of primary importance. Given the ease with which digital images and videos can be manipulated, practically they do not have any value as legal proof, if the possibility of authenticating their content is not provided. In this paper, the problem of authenticating video surveillance image sequences is considered. After an introduction motivating the need for a watermarking-based authentication of VS sequences, a brief survey of the main watermarking-based authentication techniques is presented and the requirements that an authentication algorithm should satisfy for VS applications, are discussed. A novel algorithm which is suitable for VS visual data authentication is also presented and the results obtained by applying it to test data are discussed.

Region-based image watermarking

Abstract: We introduce a novel method for embedding and detecting a chaotic watermark in the digital spatial image domain, based on segmenting the image and locating regions that are robust to several image manipulations. The robustness of the method is confirmed by experimental results that display the immunity of the embedded watermark to several kinds of attacks, such as compression, filtering, scaling, cropping, and rotation.

Content-based video parsing and indexing based on audio-visual interaction

Abstract: A content-based video parsing and indexing method is presented in this paper, which analyzes both information sources (auditory and visual) and accounts for their inter-relations and synergy to extract high-level semantic information. Both frame- and object-based access to the visual information is employed. The aim of the method is to extract semantically meaningful video scenes and assign semantic label(s) to them. Due to the temporal nature of video, time has to be accounted for. Thus, time-constrained video representations and indices are generated. The current approach searches for specific types of content information relevant to the presence or absence of speakers or persons. Audio-source parsing and indexing leads to the extraction of a speaker label mapping of the source over time. Video-source parsing and indexing results in the extraction of a talking-face shot mapping over time. Integration of the audio and visual mappings constrained by interaction rules leads to higher levels of video abstraction and even partial detection of its context.

A benchmarking protocol for watermarking methods

Abstract: A benchmarking system for watermarking algorithms is described. The proposed benchmarking system can be used to evaluate the performance of watermarking methods used for copyright protection, authentication, fingerprinting, etc. Although the system described is used for image watermarking, the general framework can be used, by introducing a different set of attacks, for benchmarking of video and audio data.

Nonlinear Model-Based Image/Video Processing and Analysis

Abstract: From the Publisher: This book presents a collection of papers on various topics related to image and video processing. Both topics are of great interest to the image processing community, and video processing technologies are of particular interest to the multimedia and aerospace industries.

Robust spatial image watermarking using progressive detection

Abstract: A novel method for image watermarking robust to geometric distortions is proposed. A binary watermark is embedded in a grayscale or a color host image. The ability of progressive watermark detection enables fast and robust watermark detection even after several geometric distortions of the watermarked image. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks. Experiments conducted using the Stirmark benchmarking tests, indicate the superiority of the proposed method.

Combining support vector machines for accurate face detection

Abstract: The paper proposes the application of majority voting on the output of several support vector machines in order to select the most suitable learning machine for frontal face detection. The first experimental results indicate a significant reduction of the rate of false positive patterns.

Occlusion resistant object tracking

Abstract: Object tracking with occlusion prediction using multiple feature correspondences is proposed. The tracking region is defined by a set of point features, tracked using Kanade-Lucas-Tomasi (1991) algorithm. During total occlusion the region position is estimated using motion prediction based on a Kalman filtering scheme applied to the motion model prior to occlusion. During partial occlusion the displacements of the occluded features are predicted based on the motion of the bounding box of the moving object. Experimental results on real and artificial images have shown that the algorithm behaves well under total and partial occlusion.

Digital image processing in painting restoration and archiving

Abstract: Digital image processing and analysis can be an important tool for the restoration of works of art. This paper presents three applications of image processing in this field: a method for digital crack restoration of paintings, a technique for color restoration of old paintings and a method for mosaicing of partial images of works of art painted on curved surfaces. A digital archiving system for works of arts is also described.

Statistical analysis of Markov chaotic sequences for watermarking applications

Abstract: In this paper, statistical analysis of watermarking schemes based on correlation detection is presented. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are exploited, resulting in superior watermark detection reliability. Correlation/spectral properties of such sequences are easily controllable, a fact that reflects on the watermarking system performance.

Frontal face detection using support vector machines and back-propagation neural networks

Abstract: Face detection is a key problem in building systems that perform face recognition/verification and model-based image coding. Two algorithms for face detection that employ either support vector machines or backpropagation feedforward neural networks are described, and their performance is tested on the same frontal face database using the false acceptance and false rejection rates as quantitative figures of merit. The aforementioned algorithms can replace the explicitly-defined knowledge for facial regions and facial features in mosaic-based face detection algorithms.

Bernoulli shift generated chaotic watermarks: theoretic investigation

Abstract: The paper statistically analyzes the behaviour of chaotic watermark signals generated by n-way Bernoulli shift maps. For this purpose, a simple blind copyright protection watermarking system is considered. The analysis involves theoretical evaluation of the system detection reliability, when a correlator detector is used. The aim of the paper is twofold: (i) to introduce the n-way Bernoulli shift generated chaotic watermarks and theoretically contemplate their properties with respect to detection reliability and (ii) to establish theoretically their potential superiority against the widely used pseudorandom watermarks. Experimental verification of the theoretical analysis results is also performed.

Scene change detection based on audio-visual analysis and interaction

Abstract: A scene change detection method is presented in this paper, which analyzes both auditory and visual information sources and accounts for their inter-relations and coincidence to semantically identify video scenes. Audio analysis focuses on the segmentation of the audio source into three types of semantic primitives, i.e. silence, speech and music. Further processing on speech segments aims at locating speaker change instants. Video analysis attempts to segment the video source into shots, without the segmentation being affected by camera pans, zoom-ins/outs or significantly high object motion. Results from single source segmentation are in some cases suboptimal. Audio-visual interaction achieves to either enhance single source findings or extract high level semantic information. The aim of this paper is to identify semantically meaningful video scenes by exploiting the temporal correlations of both sources based on the observation that semantic changes are characterized by significant changes in both information sources. Experimentation has been carried on a real TV serial sequence composed of many different scenes with plenty of commercials appearing in-between. The results are proven to be rather promising.

Robust RBF networks

Abstract: We introduce training algorithms for Radial Basis Function (RBF) networks using robust statistics. The proposed training algorithms have two stages. The first stage rely on a robust learning vector quantization approach which estimates the hidden unit weights. The second stage employs backpropagation for the output weight calculation. We introduce the Median RBF (MRBF) training algorithm and Alpha-Trimmed Mean RBF. The efficiency of MRBF and classical training using learning vector quantization are compared in estimating overlapping Gaussian distributions. Applications to artificial data classification and object modeling are provided for the proposed algorithms. 1 Introduction Radial Basis Functions (RBF) have been used in several applications for functional modeling and pattern classification. They have been found to have very good functional approximation capabilities. It has been proven that any continuous function can be modeled up to a certain precision by a set of radial basis functions [1], [2], [3]. RBFs have their fundamentals drawn from probability function estimation theory.

Pseudoautomatic lip contour detection based on edge direction patterns

Abstract: Detection and tracking of the lip contour is an important issue in lipreading. While there are solutions for lip tracking once a good contour initialization in the first frame is available, the problem of finding such a good initialization is not yet solved automatically, but done manually. Solutions based on edge detection and tracking have failed when applied to real world mouth images. In this paper, we propose a solution to lip contour detection that minimizes user interaction by requiring a minimal number of points to be marked manually on the mouth image. The proposed approach is based on edge detection using gradient masks and edge following. The method is based on the examination of gradient direction patterns in the lip area, and makes use of the local direction constancy along the lip contours, as opposed to the other regions of the mouth image that are characterized by random edge directions.

Virtual drilling-sculpturing in 3-D volumes

Abstract: In this paper we propose a virtual drilling-sculpturing algorithm applied on 3-D objects. The 3-D objects can either be simple geometrical shapes such as cubes and spheres or more complicated objects as teeth. In the second case, we consider that we are provided with a sparse set of parallel and equi-distant slices of the 3-D object. With that set of slices we reconstruct the 3-D object using a volumetric interpolation method. On the created volumes, we simulate the drilling action as a 3-D erosion operation. The proposed technique is applied for virtual drilling of teeth considering various bur tools of different shapes as erosion elements. Furthermore, the algorithm has been extended as a virtual sculpturing method that can be applied in many 3-D objects, simulating the action of chisel tools.

Theoretic Performance Analysis of a Watermarking System based on Bernoulli Chaotic Sequences

Abstract: The paper statistically analyzes the behaviour of a simple blind copyright protection watermarking system based on chaotic watermark signals generated by n-way Bernoulli shift maps. The analysis involves theoretic evaluation of the system detection reliability, when a correlator detector is used. The effect of simple distortions on the detection reliability is also theoretically investigated. The aim of the paper is twofold: (i) to introduce the n-way Bernoulli shift generated chaotic watermarks and theoretically contemplate their properties with respect to detection reliability and (ii) to theoretically establish their potential superiority against the widely used pseudorandom watermarks.

Support vector machines on the space of Walsh functions and their properties

Abstract: Support vector machine is a special kind of learning machine, proposed by Vapnik. The learning capability of support vector machines depends on the Vapnik-Chervonenkis (VC) dimension of the kernel function used. In this paper, we construct a new kernel function for support vector machine, which is based on Walsh functions. We prove some theoretical results related to the VC-dimension of the support vector machines which are built in the space of the Walsh functions. First experimental results for face detection are reported.

Mm-websom: a variant of websom based on order statistics

Abstract: A variant of the WEBSOM architecture for information retrieval is proposed in this paper. WEBSOM is based on the self-organizing map that employs a linear LMS adaptation rule for updating the weight vector of each neuron. Accordingly, the weight vector converges asymptotically to the conditional cluster mean of the feature vectors assigned to the class represented by the weight vector of the neuron. We propose to replace the updating rule by employing the marginal median. The objective is to overcome the drawbacks of the standard technique in the presence of outliers in the training set and to use robust estimators of the reference vectors for each class. Experimental results demonstrate a superior performance of the proposed variant against the standard algorithm, in terms of the number of training iterations needed so that the mean square error (i.e., the average distortion) drops to the 1 e of its initial value. We provide precision-recall curves as a measure of the quality of the clustering procedur as well. Both techniques are tested using a corpus that comprises web pages selected over the Internet.

Virtual Drilling in 3-D Objects Reconstructed by Shape-Based Interpolation

Abstract: In this paper we propose a virtual drilling algorithm which is applied on 3-D objects. We consider that initial we are provided with a sparse set of parallel and equi-distant slices of a 3-D object. We propose a volumetric interpolation algorithm for recovering the 3-D shape from the given set of slices. This algorithm employs a morphology morphing transform. Drilling is simulated on the resulting volume as a 3-D erosion operation. The proposed technique is applied for virtual drilling of teeth considering various burr shapes as erosion elements.

Document Organization and Retrieval using Self Organizing Maps and Statistical Language Modeling

Abstract: In this paper we present a method for document organization and retrieval based on statistical language modeling.The proposed method, which is based on the vector model, uses nonlinear interpolation to provide more accurate statistical estimators of the conditional probabilities employed for encoding the context of each word. An information retrieval system is built using the self-organizing map algorithm. In the rst step, the self-organizing architecture is used to cluster the feature vectors and to build clusters of semantically related words. Subsequently, the collection of documents is encoded into vectors and the same algorithm is used to cluster the documents in contextually related classes. The information retrieval system is queried using a sample document and the corresponding precision-recall curve is provided.

Alignment of serially acquired slices using a global energy function

Abstract: An accurate, computationally efficient and fully-automated algorithm for the alignment of 2D serially acquired sections forming a 3D volume is presented. The method accounts for the main shortcomings of 3D image alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices, non parallel or missing slices. The approach relies on the optimization of a global energy function, based on the object shape, measuring the similarity between a slice and its neighborhood in the 3D volume. Slice similarity is computed using the distance transform measure in both directions. No particular direction is privileged in the method avoiding global offsets, biases in the estimation and error propagation. The method was evaluated on real images (medical and biological 3D data) and the experimental results demonstrated the method's accuracy as reconstruction errors are less than 1 degree in rotation and less than 1 pixel in translation.